Analytics

Privacy and security are top priorities for Unravel and our customers. At Unravel, we help organizations better understand and improve the performance, quality, and cost efficiency of their data and AI pipelines. As a data business, we appreciate the scope and implications of privacy and security threats. This data sheet provides details to help information security (InfoSec) teams make informed decisions. Specifically, it includes: For additional details, please reach out to our security experts.

At Snowflake, we are committed to helping customers derive meaningful insights from data with simplicity and speed. That’s why we’re thrilled to announce the general availability of our enhanced RANGE-based window frame, which will help accelerate time-series analytics. For customers across industries, the ability to perform time-series analytics is crucial. This often involves calculating rolling statistics that remain robust despite gaps or nonuniform time-series data.

At Confluent, we are committed to enhancing our product to meet the evolving needs of our customers. In the last year we’ve seen substantial increases in both UI users and UI usage. With such high growth, we’ve been looking for ways to improve experiences where we can. Based on internal and external validation, the Confluent Cloud Homepage was specifically identified as an area that could be improved.

Generative AI doesn’t just work with unstructured data.

The Snowflake AI Data Cloud is an end-to-end platform that supports all types of data, compute, use cases and personas across an entire organization. By delivering a single, unified platform for all users, it is no surprise that organizations continue to expand their use cases on Snowflake. And therefore, it is extremely important for us to reaffirm our commitment to price-performant queries for our customers on a consistent basis.

Over the past several years, data leaders asked many questions about where they should keep their data and what architecture they should implement to serve an incredible breadth of analytic use cases. Vendors with proprietary formats and query engines made their pitches, and over the years the market listened, and data leaders made their decisions.

Welcome to the latest entry in Yellowfin Japan’s ‘How to?’ blog series! This series of blogs aims to provide your team with another hands-on example of adding data visualization to your Yellowfin dashboards, using our array of chart and graph types. In our previous blog, we created a heat grid by aggregating on the basis of Day of Week and Time of Day. By sorting the axes of the chart, we were able to create an easy-to-read chart lined up from Sunday to Saturday and from 0:00 to 23:00.